AI Duration 5 days – 35 hrs
Overview
This hands-on training course is designed for professionals who have a basic understanding of Artificial Intelligence (AI) but lack experience in its development and backend functionality. The course provides a practical introduction to how AI systems work—from foundational concepts to building simple AI models using popular frameworks. Participants will explore the AI development lifecycle, gain exposure to machine learning concepts, and understand how data, algorithms, and tools come together to create intelligent applications.
Objectives
- Understand Microsoft Copilot’s core features, AI capabilities, and integration with Office apps.
- Create and refine documents, reports, and summaries in Word using AI prompts.
- Analyze datasets, create charts, and automate tasks in Excel with natural language.
- Draft and manage emails, schedules, and follow-ups effectively in Outlook.
- Build dynamic presentations and improve visual storytelling in PowerPoint.
- Leverage Copilot in Teams for meeting notes, action points, and enhanced collaboration.
- Apply AI productivity principles in different roles such as HR, Sales, and Operations.
- Practice AI prompt engineering and evaluate content for accuracy and relevance.
Audience
- Developers and IT professionals with limited AI development experience
- Technical team members looking to build AI capabilities from the ground up
- Business professionals with a general understanding of AI concepts seeking deeper technical skills
Prerequisites
- General understanding of AI and IT systems
- Basic programming knowledge (preferably Python) is helpful but not required
- No prior experience with AI development necessary
Course Content
Foundations of Artificial Intelligence
- Introduction to AI, ML, and Deep Learning
- Key AI applications across industries
- Understanding data’s role in AI
- Overview of AI development workflow
- Ethical AI and responsible development
AI Development Concepts and Tools
- Introduction to Python for AI
- Data collection and preprocessing
- Supervised vs. Unsupervised Learning
- Introduction to machine learning algorithms (Regression, Classification)
- Tools and frameworks: Jupyter Notebook, Scikit-learn, TensorFlow basics
Building and Testing AI Models
- Hands-on: building a basic AI model (e.g., prediction or classification)
- Training and evaluating models
- Understanding AI backend architecture
- Deploying a simple model using Flask or Streamlit
- Group activity: ideating an AI use case in your organization
